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Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability Yong Ren*, Jingbei Li*, Haiyang Sun, Yujie Chen, Cheng Yi, Yechang Huang, Hao Gu, Ye Bai, Xuerui Yang ICML 2026
| Model | CER (%) ↓ | MCLP (W. History) ↑ | MCLP (W/O. History) ↑ | MOS ↑ |
|---|---|---|---|---|
| GPT-Audio | 11.974 | -4.849 | -4.836 | 1.752 |
| MiMo-Audio-7B | 10.605 | -4.753 | -4.745 | 2.471 |
| Step-Audio-2-mini | 3.276 | -4.829 | -4.823 | 1.707 |
| MCLP-RPTTS (Ours) | 1.130 | -4.636 | -4.687 | 3.646 |
1# Clone the inference code
2git clone https://github.com/y-ren16/MCLP.git
3cd MCLP
4
5# Run role-play TTS inference
6python generate_roleplay_stepaudio2_multigpu.py \
7 --model-path /path/to/MCLP-RPTTS \
8 --input-jsonl /path/to/WenetSpeech-RP/eval/eval_w_history.jsonl \
9 --output-dir ./outputs/roleplay_tts \
10 --audio-base /path/to/extracted_test_audio \
11 --prompt-base /path/to/WenetSpeech-RP/eval/audio \
12 --gpus 1pip install transformers==4.49.0 torchaudio librosa onnxruntime s3tokenizer diffusers hyperpyyaml numpy| Resource | Link |
|---|---|
| 📑 Paper | arXiv:2601.22661 |
| 💻 Inference Code | github.com/y-ren16/MCLP |
| 📊 WenetSpeech-RP Dataset | huggingface.co/datasets/y-ren16/WenetSpeech-RP |
| 🔢 MCLP-Score Model | huggingface.co/y-ren16/MCLP-Score |
1@inproceedings{ren2026mclp,
2 title={Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability},
3 author={Ren, Yong and Li, Jingbei and Sun, Haiyang and Chen, Yujie and Yi, Cheng and Huang, Yechang and Gu, Hao and Bai, Ye and Yang, Xuerui},
4 booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
5 year={2026}
6}